Improving the Efficiency of Outbound CATI as a Nonresponse Follow-Up Mode in Address-Based Samples: A Quasi-Experimental Evaluation of a Dynamic Adaptive Design

This article evaluates the use of dynamic adaptive design methods to target outbound computer-assisted telephone interviewing (CATI) implemented in the 2022 California Health Interview Survey (CHIS).

Findings: Authors found that the adaptive design reduced the mean number of calls per sampled unit by about 14 percent (relative to a modeled no-adaptive-design counterfactual) with a minimal reduction in the completion rate and no strong evidence of changes in the prevalence of target demographics. This suggests that response propensity modeling can meaningfully distinguish between ABS sample units for which additional dialing is and is not productive, helping to control outbound dialing costs without compromising sample representativeness.

This article is part of JSSAM's Special Issue on Innovations in Mixed-Mode Surveys.